A Two-Stage Model for Data-Driven Leakage Detection and Localization in Water Distribution Networks

Abstract

Water utilities face the challenge of reducing water losses by promptly detecting, localizing, and repairing leaks during their operational stage. To address this challenge, utilities are exploring alternative approaches to detect leaks with high accuracy in a timely manner, while minimizing environmental and economic consequences. This research proposes a two-stage model that relies on data analysis to predict leak incidents and their specific locations in water distribution networks (WDNs). By leveraging pressure and flow rate data collected from multiple points in the network, the model first calculates prediction errors in pressure heads. Subsequently, statistical measures applied to these error distributions are used to classify the occurrence and location of leaks. The suggested approach is both cost-effective and easily deployable. Through simulation-based case studies conducted on various benchmark networks, the efficacy of the proposed model is demonstrated. The results show that the model effectively predicts leak occurrences and their respective locations. However, it should be noted that as the network size increases, the model’s performance diminishes, resulting in reduced accuracy. Later, the accuracy of leak prediction has been evaluated by examining its sensitivity to varying numbers of sensors and different levels of noise.

Keywords

• machine learning for leak detection
• leak localization
• water distribution network
• sensors for leak detection